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Trajectory Prediction And Near Miss Detection Using Social Distancing Monitoring And Distance Based Proximal Indicators

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dc.contributor.authorLim, Lek Ming
dc.date.accessioned2026-09-18T00:37:46Z
dc.date.available2026-09-18T00:37:46Z
dc.date.issued2025-07
dc.description.abstractDespite efforts to improve road safety, accidents persist due to insufficient evidence from manual police reporting, non-optimized detection algorithms, and technical limitations in real-time video processing and modelling. This study focuses on detecting and tracking vehicles within a monitoring system and analysing near-miss incidents (black spot and blind spot), specifically examining the influence of video quality on detection performance using advanced model detectors (YOLOv3-tiny, YOLOv4-tiny, YOLOv5, YOLOv7, YOLOv7+G3HN, YOLOv7+CNeB, and Faster RCNN). The experiment employed methods for vehicle detection and trajectory prediction through the monitoring system.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/25031
dc.language.isoen
dc.subjectTrajectory Prediction And Near Miss Detection
dc.subjectSocial Distancing Monitoring And Distance Based Proximal Indicators
dc.titleTrajectory Prediction And Near Miss Detection Using Social Distancing Monitoring And Distance Based Proximal Indicators
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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